Digital elevation modeling using artificial neural networks, deterministic and geostatistical interpolation methods

Authors : Esra Aslı ÇUBUKÇU, Vahdettin DEMİR, Mehmet Faik SEVİMLİ

Pages : 199-205

Doi:10.31127/tuje.889570

View : 15 | Download : 4

Publication Date : 2022-07-20

Article Type : Research Paper

Abstract :The digital elevation model insert ignore into journalissuearticles values(DEM); is the name given to a digital structure used to indicate the surface. Determination of features such as elevation, basin slope and basin area are very important in engineering applications. These properties are determined by the DEM and their power to represent accuracy or truth is vital in engineering applications. In addition to the latitude insert ignore into journalissuearticles values(X);, longitudeinsert ignore into journalissuearticles values(Y); coordinate information, altitude information is required, and intermediate values are determined by different methods for DEM. In this study, Mert River Basin Samsun insert ignore into journalissuearticles values(Turkey); was chosen as the application area. Heights are estimated from X, Y coordinate information. Three different Artificial Neural Networks, IDW and Kriging methods were used. Artificial Neural Networks insert ignore into journalissuearticles values(ANN); were analyzed with three different inputs. These are: insert ignore into journalissuearticles values(i); x coordinate information; insert ignore into journalissuearticles values(ii); y coordinate information; insert ignore into journalissuearticles values(iii); It is in the form of x and y coordinate information and are used Radial Based Artificial Neural Network, Multilayer Artificial Neural Network and Generalized Artificial Neural Network. X and Y coordinate information was used in IDW and Kriging interpolation methods. Results were evaluated using Coefficient of Determination insert ignore into journalissuearticles values(R²);, Mean Absolute Error insert ignore into journalissuearticles values(MAE); and Root Mean Square Error insert ignore into journalissuearticles values(RMSE); as comparison criteria. According to the modeling results: It was observed that the results of all methods reached a sufficient level of accuracy. Kriging method was found to be the most successful model, followed by IDW and ANN. 
Keywords : Digital Elevation Models, Artificial Neural Network, Samsun, Geostatistical methods, Mert River Basin